The CIFAR-10 images are first transformed by passing them through a pre-trained Inception network and extracting the vision-relevant features at the final pool3 layer (as is done when computing the Fréchet Inception distance (FID) for evaluating GANs [1]}) using code from [2]} (Apache License 2.0). These features are then also reduced to an 8-D embedding using UMAP with default settings as above. As in MNIST, synthetic data are obtained by sampling from a pre-trained MMD GAN [3]}. Samples are automatically labeled by training the ResNet-18 classifier obtained from https://github.com/kuangliu/pytorch-cifar (MIT License) and taking its predictions on the samples. Each party \(i\)  has a dataset \(D_i\)  of size 5K, and \(|G|=100\) K synthetic data points are generated.
